Graphical Models for Human Motion Modelling

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. To automate the process of motion modeling we consider a class of learned dynamic models cast in the framework of dynamic Bayesian networks (DBNs) applied to analysis and tracking of the human figure. While direct learning of DBN parameters is possible, Bayesian learning formalism suggests that hyperparametric model description that considers all possible model dynamics may be preferred. Such integration over all possible models results in a subspace embedding of the original motion measurements. To this end, we propose a new family of Marginal Auto-Regressive (MAR) graphical models that describe the space of all stable auto-regressive sequences, regardless of their specific dynamics. We show that the use of dynamics and MAR models may lead to better estimates of sequence subspaces than the ones obtained by traditional non-sequential methods. We then propose a learning method for estimating general nonlinear dynamic system models that utilizes the new MAR models. The utility of the proposed methods is tested on the task of tracking 3D articulated figures in monocular image sequences. We demonstrate that the use of MAR can result in efficient and accurate tracking of the human figure from ambiguous visual inputs.